'''simple docstring'''




import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

__UpperCAmelCase					=logging.getLogger()






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       int:
    __lowerCamelCase            =    {}
    __lowerCamelCase            =    os.path.join(A_							,    '''all_results.json'''							)
    if os.path.exists(A_							):
        with open(A_							,    '''r'''							) as f:
            __lowerCamelCase            =    json.load(A_							)
    else:
        raise ValueError(f"""can\'t find {path}"""							)
    return results


__UpperCAmelCase					=logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




@require_torch_tpu
class        a__ (  _A					):






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         import xla_spawn

         __lowerCamelCase            =    self.get_auto_remove_tmp_dir()
         __lowerCamelCase            =    f"""
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

         with patch.object(UpperCamelCase__    ,							'''argv'''    ,							UpperCamelCase__       ):
             __lowerCamelCase            =    time()
             xla_spawn.main()
             __lowerCamelCase            =    time()

             __lowerCamelCase            =    get_results(UpperCamelCase__       )
             self.assertGreaterEqual(result['''eval_accuracy''']    ,							0.75       )

             # Assert that the script takes less than 500 seconds to make sure it doesn't hang.
             self.assertLess(end - start    ,							5_00       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						int       ):





         """simple docstring"""



         import xla_spawn

         __lowerCamelCase            =    '''
            ./tests/test_trainer_tpu.py
            --num_cores=8
            ./tests/test_trainer_tpu.py
            '''.split()
         with patch.object(UpperCamelCase__    ,							'''argv'''    ,							UpperCamelCase__       ):
             xla_spawn.main()


def a__       (			A_=28123   ):






  '''simple docstring'''





  __magic_name__								=			[1] * (limit + 1)

  for i in range(2,	int(limit**0.5   ) + 1   ):
    sum_divs[i * i] += i
    for k in range(i + 1,	limit // i + 1   ):
      sum_divs[k * i] += k + i

  __magic_name__								=			set()
  __magic_name__								=			0

  for n in range(1,	limit + 1   ):
    if sum_divs[n] > n:
      abundants.add(A_   )

    if not any((n - a in abundants) for a in abundants   ):
      res += n

  return res


if __name__ == "__main__":
    print(solution())

